SoroushVahidi/scidocs huggingface dataset:
Intelligent stereo camera mobile platform for indoor service robot research
(2016)

Stereo vision is an active research topic in computer vision. Point Grey® Bumblebee® and digital single-lens reflex camera (DSLR) are normally found in the stereo vision research, they are robust but expensive. Open source electronic prototyping platforms such as Arduino and Raspberry Pi are interesting products, which allows students or researchers to custom made inexpensive experimental equipment for their research projects. This paper describes the intelligent stereo camera mobile platform developed in our research using Pi and camera modules and presents the concept of using inexpensive open source parts for robotic stereo vision research work in details.
Merging and Splitting Eigenspace Models
(2000)

We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are more efficient than computing new eigenspace models directly from the observations when the eigenmodels are dimensionally small compared to the total number of observations. Such methods are important because they provide a basis for novel techniques in machine learning, using a dynamic split-andmerge paradigm to optimally cluster observations. Here we present a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the on-line construction of Gaussian mixture models.
An Embedded Declarative Language for Hierarchical Object Structure Traversal
(2008)

A common challenge in processing large domain-specific models and in-memory object structures (e.g., complex XML documents) is writing traversals and queries on them. Object-oriented (OO) designs, particularly those based on the Visitor pattern, are commonly used for developing traversals. However, such OO designs limit the reusability and independent evolution of visitation actions (i.e., the actions to be performed at each traversed node) due to tight coupling between the traversal logic and visitation actions, particularly when a variety of different traversals are needed. Code generators developed for traversal specification languages alleviate some of these problems but their high cost of development is often prohibitive. This paper presents Language for Embedded quEry and traverSAl (LEESA), which provides a generative programming approach for embedding object structure queries and traversal specifications within a host language, C++. By virtue of being declarative, LEESA significantly reduces the development cost of programs operating on complex object structures compared to the traditional techniques.
Design of Broadband Linear and Efficient Power Amplifier for Long-Term Evolution Applications
(2013)

In this letter, a broadband linear and efficient power amplifier for base-stations in long-term evolution (LTE) applications is designed, fabricated, and experimentally verified, employing Cree's CGH40010F GaN HEMT. A novel method of baseband, fundamental and harmonic impedances control is used to provide high linearity and efficiency across a broad bandwidth. When producing a two-tone carrier-to-intermodulation ratio (C/I) of 30 dBc, the circuit has demonstrated a two-tone power added efficiency (PAE) between 45%-60% across the frequency range from 1.6 to 2.6 GHz while delivering 36.0-38.5 dBm average output power. For a single-carrier 20 MHz LTE signal with a peak-to-average ratio (PAR) of 6.5 dB, a measured high PAE of 40%-55% can be achieved at an average output power of 35.3-37.5 dBm with an adjacent channel leakage ratio (ACLR) of about -30 dBc from 1.6 to 2.6 GHz.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
(2017)

Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper, we make contributions to audio tagging in two parts, respectively, acoustic modeling and feature learning. We propose to use a shrinking deep neural network DNN framework incorporating unsupervised feature learning to handle the multilabel classification task. For the acoustic modeling, a large set of contextual frames of the chunk are fed into the DNN to perform a multilabel classification for the expected tags, considering that only chunk or utterance level rather than frame-level labels are available. Dropout and background noise aware training are also adopted to improve the generalization capability of the DNNs. For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep denoising auto-encoder syDAE or asyDAE to generate new data-driven features from the logarithmic Mel-filter banks features. The new features, which are smoothed against background noise and more compact with contextual information, can further improve the performance of the DNN baseline. Compared with the standard Gaussian mixture model baseline of the DCASE 2016 audio tagging challenge, our proposed method obtains a significant equal error rate EER reduction from 0.21 to 0.13 on the development set. The proposed asyDAE system can get a relative 6.7% EER reduction compared with the strong DNN baseline on the development set. Finally, the results also show that our approach obtains the state-of-the-art performance with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while EER of the first prize of this challenge is 0.17.
Wireless health monitoring system for patients
(2015)

eHealth is the provision of healthcare with the inclusion of telecommunication techniques. This project looks at the construction of a simple device that will be capable of transferring the data of a patient's vital signs to a remote device wirelessly. The necessity of this project is to alleviate the difficulty that is encountered by medical experts in monitoring multiple patients simultaneously. This project will enable them to observe patients without having to be physically present at their bedside, be it in the hospital or in their home. A patient's body temperature, heart rate and electrocardiography (ECG) are transferred wirelessly through an agent such as Bluetooth technology.
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
(2017)

Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs, using back-propagation through time, often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.
SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions
(2011)

In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the traditional database approaches. Hence, it is not straightforward to adapt successful database techniques for RDF federation. Reasons are the missing cooperation between SPARQL endpoints and the need for detailed data statistics for estimating the costs of query execution plans. We have implemented SPLENDID, a query optimization strategy for federating SPARQL endpoints based on statistical data obtained from voiD descriptions [1].
Industrial IoT lifecycle via digital twins
(2016)

Currently, the IoT discussion is focused primarily on the operational phase. This includes how a IoT device behaves, operates, communicates, and interacts with other IoT devices during operation. However, IoT devices and systems have other lifecycle phases before and after operation. This extended abstract provides an overview of how other IoT lifecycle phases (e.g., design and service) can be improved with information feedback and feedforward flows between them. Digital Twins are a new mechanism to manage IoT devices and IoT systems-of-systems throughout their lifecycle. We present our vision on the industrial IoT lifecycle managed and optimized at scale via Digital Twins.
A Feature Learning Based Approach for Automated Fruit Yield Estimation
(2013)

This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on treecrops. The learning approach makes the algorithm flexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R2 = 0.81.
Threat awareness for critical infrastructures resilience
(2016)

Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.
A 3D human motion refinement method based on sparse motion bases selection
(2016)

Motion capture (MOCAP) is an important technique that is widely used in many areas such as computer animation, film industry, physical training and so on. Even with professional MOCAP system, the missing marker problems always occur. Motion refinement is an essential preprocessing step for MOCAP data based applications. Although many existing approaches for motion refinement have been developed, it is still a challenging task due to the complexity and diversity of human motion. A data driven based motion refinement method is proposed in this paper, which modifies the traditional sparse coding process for special task of motion recovery from missing parts. Meanwhile, the objective function is derived by taking both statistical and kinematical property of motion data into account. Poselet model and moving window grouping are applied in the proposed method to achieve a fine-grained feature representation, which preserves the embedded spatial-temporal kinematic information. 5 motion dictionaries are learnt for each kind of poselet from training data in parallel. The motion refine problem is finally solved as an ℓ1-minimization problem. Compared with several state-of-art motion refine methods, the experimental result shows that our approach outperforms the competitors.
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
(2015)

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.
Omnidirectional conformal antenna array based on E-shaped patches
(2014)

An antenna consisting of 4 element subarrays of strongly coupled E-shaped patches placed conformally on a metal cylinder is proposed for use in wireless local area networks (WLAN). The use of this special array configuration in the cylindrical case allows reaching a 8.3 % bandwidth and an omnidirectional radiation pattern in the horizontal plane with only 2 subarrays. CST Microwave Studio is used for validation purposes before manufacturing. To the knowledge of the authors, it is the first time that this recently introduced concept of strongly coupled E-shaped patches is used in a conformal antenna.
Recurrent Memory Array Structures Technical Report
(2016)

The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves stateof-the-art performance on character level text prediction achieving 1.402 BPC† on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.
Information Fusion in the Immune System
(2010)

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.
Synthetic Aperture Radar Imaging Using Stepped Frequency Waveform
(2012)

This paper presents a synthetic aperture radar (SAR) imaging system using a stepped frequency waveform. The main problem in stepped frequency SAR is the range difference in one sequence of pulses. This paper analyzes the influence of range difference in one sequence of pulses in detail and proposes a method to compensate this influence in the Doppler domain. A Stolt interpolation for the stepped frequency signal is used to focus the data accurately. The parameters for the stepped frequency SAR are analyzed, and some criteria are proposed for system design. Finally, simulation and experimental results are presented to demonstrate the validity of the proposed method. The experimental data are collected by using a stepped frequency radar mounted on a rail.
Follow me cloud: interworking federated clouds and distributed mobile networks
(2013)

This article introduces the Follow-Me Cloud concept and proposes its framework. The proposed framework is aimed at smooth migration of all or only a required portion of an ongoing IP service between a data center and user equipment of a 3GPP mobile network to another optimal DC with no service disruption. The service migration and continuity is supported by replacing IP addressing with service identification. Indeed, an FMC service/application is identified, upon establishment, by a session/service ID, dynamically changing along with the service being delivered over the session; it consists of a unique identifier of UE within the 3GPP mobile network, an identifier of the cloud service, and dynamically changing characteristics of the cloud service. Service migration in FMC is triggered by change in the IP address of the UE due to a change of data anchor gateway in the mobile network, in turn due to UE mobility and/or for load balancing. An optimal DC is then selected based on the features of the new data anchor gateway. Smooth service migration and continuity are supported thanks to logic installed at UE and DCs that maps features of IP flows to the session/service ID.
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study
(2013)

Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.
Task and context determine where you look.
(2007)

The deployment of human gaze has been almost exclusively studied independent of any specific ongoing task and limited to two-dimensional picture viewing. This contrasts with its use in everyday life, which mostly consists of purposeful tasks where gaze is crucially involved. To better understand deployment of gaze under such circumstances, we devised a series of experiments, in which subjects navigated along a walkway in a virtual environment and executed combinations of approach and avoidance tasks. The position of the body and the gaze were monitored during the execution of the task combinations and dependence of gaze on the ongoing tasks as well as the visual features of the scene was analyzed. Gaze distributions were compared to a random gaze allocation strategy as well as a specific "saliency model." Gaze distributions showed high similarity across subjects. Moreover, the precise fixation locations on the objects depended on the ongoing task to the point that the specific tasks could be predicted from the subject's fixation data. By contrast, gaze allocation according to a random or a saliency model did not predict the executed fixations or the observed dependence of fixation locations on the specific task.
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
(2009)

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
(2006)

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.
Factors affecting the acceptance of information systems supporting emergency operations centres
(2016)

Despite the recognition that information system acceptance is an important antecedent of effective emergency management, there has been comparatively very little research examining this aspect of technology acceptance. The current research responded to this gap in literature by adapting and integrating existing models of technology acceptance. This was done in order to examine how a range of technology acceptance factors could affect the acceptance of emergency operations centre information systems. Relationships between several of these factors were also examined. Questionnaire data from 383 end-users of four different emergency operations centre information systems were analysed using structural equation modelling. This analysis concluded that technology acceptance factors of performance expectancy, effort expectancy, social influence and information quality explained 65 percent of variance in symbolic adoption, which is a combination of mental acceptance and psychological attachment towards an information system. A number of moderating effects of age, gender, experience of use and domain experience were also identified. A mediating component, of performance expectancy, explained 49 percent of variance between facilitating conditions, information quality, effort expectancy, and resulting symbolic adoption. These findings highlight a need to re-focus technology acceptance research on both mediating and moderating effects and the importance of considering domain specific factors. Applied recommendations are also made, for successfully implementing relevant information